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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 487 records · Page 27Linked to original sources

bioRxiv2014

Wide field-of-view, twin-region two-photon imaging across extended cortical networks

We demonstrate a two-photon imaging system with corrected optics including a custom objective that provides cellular resolution across a 3.5 mm field of view (9.6 mm2). Temporally multiplexed excitation pathways can be independently repositioned in XY and Z to simultaneously image regions within the expanded field of view. We used this new imaging system to measure activity correlations between neurons in different cortical areas in awake mice.

Neuroscience↗

bioRxiv2014

A diffusive homeostatic signal maintains neural heterogeneity and responsiveness in cortical networks

Gaseous neurotransmitters such as nitric oxide (NO) provide a unique and often overlooked mechanism for neurons to communicate through diffusion within a network, independent of synaptic connectivity. NO provides homeostatic control of intrinsic excitability. Here we conduct a theoretical investigation of the distinguishing roles of NO-mediated diffusive homeostasis in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis provide a robust mechanism for maintaining stable activity following perturbations. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that these properties are preserved when homeostatic and Hebbian plasticity are combined. These results suggest a mechanism for dynamically maintaining neural heterogeneity, and expose computational advantages of non-local homeostatic processes.\n\nAuthor SummaryNeural firing rates must be maintained within a stable range in the face of ongoing fluctuations in synaptic connectivity. Existing cortical network models achieve this through various homeostatic mechanisms which constrain the excitability of individual neurons according to their recent activity. Here, we propose a new mechanism, diffusive homeostasis, in which neural excitability is modulated by nitric oxide, a gas which can flow freely across cell membranes. Information about a neurons firing rate can be carried by nitric oxide, meaning that an individual neurons excitability is affected by neighboring neurons firing rates as well as its own. We find that this allows a neuron to deviate from the target population activity, as its neighbors will counteract this deviation, thus maintaining stable average activity. This form of neural heterogeneity is more flexible than assigning different target firing rates to individual neurons.

Neuroscience↗

bioRxiv2014

NODE DETECTION USING HIGH-DIMENSIONAL FUZZY PARCELLATION APPLIED TO THE INSULAR CORTEX

Several functional connectivity approaches require the definition of a set of ROIs that act as network nodes. Different methods have been developed to define these nodes and to derive their functional and effective connections, most of which are rather complex. Here we aim to propose a relatively simple \"one-step\" border detection and ROI estimation procedure employing the fuzzy c-mean clustering algorithm.\n\nTo test this procedure and to explore insular connectivity beyond the two/three-region model currently proposed in the literature, we parcellated the insular cortex of a group of twenty healthy right-handed volunteers (10 females) scanned in a resting state condition.\n\nEmploying a high-dimensional functional connectivity-based clustering process, we confirmed the two patterns of connectivity previously described. This method revealed a complex pattern of functional connectivity where the two previously detected insular clusters are subdivided into several other networks, some of which not commonly associated with the insular cortex, such as the default mode network and parts of the dorsal attentional network. Finally, the detection of nodes was reliable as demonstrated by the confirmative analysis performed on a replication group of subjects.

Neuroscience↗

bioRxiv2014

Activation of dorsal raphe serotonergic neurons promotes persistent waiting independently of reinforcing effects

The central neuromodulator serotonin (5-HT) has been implicated in a wide range of behaviors and affective disorders, but the principles underlying its function remain elusive. One influential line of research has implicated 5-HT in response inhibition and impulse control. Another has suggested a role in affective processing. However, whether and how these effects relate to each other is still unclear. Here, we report that optogenetic activation of 5-HT neurons in the dorsal raphe nucleus (DRN) produces a dose-dependent increase in mices ability to withhold premature responding in a task that requires them to wait several seconds for a randomly delayed tone. The 5-HT effect had a rapid onset and was maintained throughout the stimulation period. In addition, movement speed was slowed but stimulation did not affect reaction time or time spent at the reward port. Using similar stimulation protocols in place preference and value-based choice tests, we found no evidence of either appetitive or aversive effects of DRN 5-HT neuron activation. These results provide strong evidence that the efficacy of DRN 5-HT neurons in promoting waiting for delayed rewards is independent of appetitive or aversive effects and support the importance of 5-HT in behavioral persistence and impulse control.

Neuroscience↗

bioRxiv2014

Networks that learn the precise timing of event sequences

Neuronal circuits can learn and replay firing patterns evoked by sequences of sensory stimuli. After training, a brief cue can trigger a spatiotemporal pattern of neural activity similar to that evoked by a learned stimulus sequence. Network models show that such sequence learning can occur through the shaping of feedforward excitatory connectivity via long term plasticity. Previous models describe how event order can be learned, but they typically do not explain how precise timing can be recalled. We propose a mechanism for learning both the order and precise timing of event sequences. In our recurrent network model, long term plasticity leads to the learning of the sequence, while short term facilitation enables temporally precise replay of events. Learned synaptic weights between populations determine the time necessary for one population to activate another. Long term plasticity adjusts these weights so that the trained event times are matched during playback. While we chose short term facilitation as a time-tracking process, we also demonstrate that other mechanisms, such as spike rate adaptation, can fulfill this role. We also analyze the impact of trial-to-trial variability, showing how observational errors as well as neuronal noise result in variability in learned event times. The dynamics of the playback process determine how stochasticity is inherited in learned sequence timings. Future experiments that characterize such variability can therefore shed light on the neural mechanisms of sequence learning.

Neuroscience↗

bioRxiv2014

Structured dynamics of neural activity across developing neocortex

The cerebral cortex exhibits spontaneous and sensory evoked patterns of activity during early development that is vital for the formation and refinement of neural circuits. Identifying the source and flow of this activity locally and globally is critical for understanding principles guiding self-organization in the developing brain. Here we use whole brain transcranial optical imaging at high spatial and temporal resolution to demonstrate that dynamical patterns of neuronal activity in developing mouse neocortex consist of spatially discrete domains that are coordinated in an age, areal, and behavior-dependent fashion. Ongoing cortical activity displays mirror-symmetric activation patterns across the cerebral hemispheres and stereotyped network architectures that are shaped during development, with parietal-sensorimotor subnetworks functionally connected to occipital regions through frontal-medial cortical areas. This study provides the first broad description of population activity in the developing neocortex at a scope and scale that bridges the microscopic and macroscopic spatiotemporal resolutions provided by traditional neurophysiological and functional neuroimaging techniques. Mesoscale maps of cortical population dynamics within animal models will be crucial for future efforts to understand and treat neurodevelopmental disorders.

Neuroscience↗

bioRxiv2014

Bridging psychology and genetics using large-scale spatial analysis of neuroimaging and neurogenetic data

Understanding how microscopic molecules give rise to complex cognitive processes is a major goal of the biological sciences. The countless hypothetical molecule-cognition relationships necessitate discovery-based techniques to guide scientists toward the most productive lines of investigation. To this end, we present a novel discovery tool that uses spatial patterns of neural gene expression from the Allen Brain Institute (ABI) and large-scale functional neuroimaging meta-analyses from the Neurosynth framework to bridge neurogenetic and neuroimaging data. We quantified the spatial similarity between over 20,000 genes from the ABI and 48 psychological topics derived from lexical analysis of neuroimaging articles, producing a comprehensive set of gene/cognition mappings that we term the Neurosynth-gene atlas. We demonstrate the ability to independently replicate known gene/cognition associations (e.g., between dopamine and reward), and subsequently use it to identify a range of novel associations between individual molecules or genes and complex psychological phenomena such as reward, memory and emotion. Our results complement existing discovery-based methods such as GWAS, and provide a novel means of generating hypotheses about the neurogenetic substrates of complex cognitive functions.

Neuroscience↗

bioRxiv2014

The neural correlates of consciousness in sleep: a no-task, within-state paradigm

Consciousness never fades during wake. However, if awakened from sleep, sometimes we report dreams and sometimes no experiences. Traditionally, dreaming has been identified with REM sleep, characterized by a wake-like, globally activated, high-frequency EEG. However, dreaming also occurs in NREM sleep, characterized by prominent low-frequency activity. This challenges our understanding of the neural correlates of conscious experiences in sleep. Using high-density EEG, we contrasted the presence and absence of dreaming within NREM and REM sleep. In both NREM and REM sleep, the presence of dreaming was associated with a local decrease in low-frequency activity in posterior cortical regions. High-frequency activity within these regions correlated with specific dream contents. Monitoring this posterior hot zone predicted the presence/absence of dreaming during NREM sleep in real time, suggesting that it may constitute a core correlate of conscious experiences in sleep.

Neuroscience↗

bioRxiv2014

Pre-synaptic inhibition of afferent feedback in the macaque spinal cord does not modulate with cycles of peripheral oscillations around 10 Hz

Spinal interneurons are partially phase-locked to physiological tremor around 10Hz. The phase of spinal interneuron activity is approximately opposite to descending drive to motoneurons, leading to partial phase cancellation and tremor reduction. Pre-synaptic inhibition of afferent feedback modulates during voluntary movements, but it is not known whether it tracks more rapid fluctuations in motor output such as during tremor.\n\nIn this study, dorsal root potentials (DRPs) were recorded from the C8 and T1 roots in two macaque monkeys following intra-spinal micro-stimulation (random inter-stimulus interval 1.5-2.5 s, 30-100A), whilst the animals performed an index finger flexion task which elicited peripheral oscillations around 10Hz. Forty one responses were identified with latency <5ms; these were narrow (mean width 0.59 ms), and likely resulted from antidromic activation of afferents following stimulation near terminals. Significant modulation during task performance occurred in 16/41 responses, reflecting terminal excitability changes generated by pre-synaptic inhibition (Walls excitability test). Stimuli falling during large-amplitude 8-12Hz oscillations in finger acceleration were extracted, and sub-averages of DRPs constructed for stimuli delivered at different oscillation phases. Although some apparent phase-dependent modulation was seen, this was not above the level expected by chance.\n\nWe conclude that although terminal excitability reflecting pre-synaptic inhibition of afferents modulates over the timescale of a voluntary movement, it does not follow more rapid changes in motor output. This suggests that pre-synaptic inhibition is not part of the spinal systems for tremor reduction described previously, and that it plays a role in overall - but not moment-by-moment - regulation of feedback gain.

Neuroscience↗

bioRxiv2014

Dynamic reorganization of brain functional networks during cognition

How cognition emerges from neural dynamics? The dominant hypothesis states that interactions among distributed brain regions through phase synchronization give basis for cognitive processing. Such phase-synchronized networks are transient and dynamic, established on the timescale of milliseconds in order to perform specific cognitive operations. But unlike resting-state networks, the complex organization of transient cognitive networks is typically not characterized within the graph theory framework. Thus, it is not known whether cognitive processing merely changes strength of functional connections or, conversely, requires qualitatively new topological arrangements of functional networks. To address this question, we recorded high-density EEG when subjects performed a visual discrimination task and conducted and event-related network analysis (ERNA) where source-space weighted functional networks were characterized with graph measures. We revealed rapid, transient, and frequency-specific reorganization of the networks topology during cognition. Specifically, cognitive networks were characterized by strong clustering, low modularity, and strong interactions between hub-nodes. Our findings suggest that dense and clustered connectivity between the hub nodes belonging to different modules is the \"network fingerprint\" of cognition. Such reorganization patterns might facilitate global integration of information and provide a substrate for a \"global workspace\" necessary for cognition and consciousness to occur. Thus, characterizing topology of the event-related networks opens new vistas to interpret cognitive dynamics in the broader conceptual framework of graph theory.

Neuroscience↗

bioRxiv2014

Faciotopy—a face-feature map with face-like topology in the human occipital face area

The occipital face area (OFA) and fusiform face area (FFA) are brain regions thought to be specialized for face perception. However, their intrinsic functional organization and status as cortical areas with well-defined boundaries remains unclear. Here we test these regions for \"faciotopy\", a particular hypothesis about their intrinsic functional organisation. A faciotopic area would contain a face-feature map on the cortical surface, where cortical patches represent face features and neighbouring patches represent features that are physically neighbouring in a face. The faciotopy hypothesis is motivated by the idea that face regions might develop from a retinotopic protomap and acquire their selectivity for face features through natural visual experience. Faces have a prototypical configuration of features, are usually perceived in a canonical upright orientation, and are frequently fixated in particular locations. To test the faciotopy hypothesis, we presented images of isolated face features at fixation to subjects during functional magnetic resonance imaging. The responses in V1 were best explained by low-level image properties of the stimuli. OFA, and to a lesser degree FFA, showed evidence for faciotopic organization. When a single patch of cortex was estimated for each face feature, the cortical distances between the feature patches reflected the physical distance between the features in a face. Faciotopy would be the first example, to our knowledge, of a cortical map reflecting the topology, not of a part of the organism itself (its retina in retinotopy, its body in somatotopy), but of an external object of particular perceptual significance.

Neuroscience↗

bioRxiv2014

Stimulus set meaningfulness and neurophysiological differentiation: a functional magnetic resonance imaging study

A meaningful set of stimuli, such as a sequence of frames from a movie, triggers a set of different experiences. By contrast, a meaningless set of stimuli, such as a sequence of TV noise frames, triggers always the same experience - of seeing TV noise - even though the stimuli themselves are as different from each other as the movie frames. We reasoned that the differentiation of cortical responses underlying the subjects experiences, as measured by Lempel-Ziv complexity (incompressibility) of functional MRI images, should reflect the overall meaningfulness of a set of stimuli for the subject, rather than differences among the stimuli. We tested this hypothesis by quantifying the differentiation of brain activity patterns in response to a movie sequence, to the same movie scrambled in time, and to TV noise, where the pixels from each movie frame were scrambled in space. While overall cortical activation was strong and widespread in all conditions, the differentiation (Lempel-Ziv complexity) of brain activation patterns was correlated with the meaningfulness of the stimulus set, being highest in the movie condition, intermediate in the scrambled movie condition, and minimal for TV noise. Stimulus set meaningfulness was also associated with higher information integration among cortical regions. These results suggest that the differentiation of neural responses can be used to assess the meaningfulness of a given set of stimuli for a given subject, without the need to identify the features and categories that are relevant to the subject, nor the precise location of selective neural responses.

Neuroscience↗

bioRxiv2015

Animal Models of Epilepsy: Legacies and New Directions

Human epilepsies encompass a wide variety of clinical, behavioral and electrical manifestations. Correspondingly, studies of this disease in nonhuman animals have brought forward an equally wide array of animal models, i.e. species and acute or chronic seizure induction protocols. Epilepsy research has a long history of comparative anatomical and physiological studies on a range of mostly mammalian species. Nonetheless, a relatively limited number of rodent models emerged as the primary choices for most epilepsy-related investigations. In many cases these animal models are selected based on convenience or tradition, though technical or experimental rationale does, and should, factor into these decisions. More complex mammalian brains and, especially, genetic model organisms including zebrafish have been studied less but offer significant advantages that are being widely recognized.

Neuroscience↗

bioRxiv2014

The Neural Marketplace: I. General Formalism and Linear Theory

We propose a mathematical theory for how networks of neurons in the brain self-organize into functional networks, similarly to the self-organization of supply networks in a free-market economy. The theory is inspired by recent experimental results showing how information about changes to output synapses can travel backward along axons to affect a neurons inputs. In neuronal development, competition for such \"retroaxonal\" signals determines which neurons live and which die. We suggest that in adults, an analogous form of competition occurs between neurons, to supply their targets with appropriate information in exchange for a \"payment\" returned to them backward along the axon. We review experimental evidence suggesting that neurotrophins may constitute such a signaling pathway in the adult brain.\n\nWe construct a mathematical model, in which a small number of \"consumer\" neurons receive explicit fast error signals while a larger number of \"producer\" neurons compete to supply them with information, guided by retroaxonal signals from the consumers and from each other. We define a loss function to measure network performance and define the \"worth\" of a producer to be the increase in loss that would result if that neuron were to fall silent. We show how slow retroaxonal signals can allow producers to estimate their worth, and how these estimates allow the network to perform a form of parallel search over multiple producer cells. We validate our approximations and demonstrate the proposed learning rule using simulations.

Neuroscience↗

bioRxiv2015

Asynchronous Rate Chaos in Spiking Neuronal Circuits

The brain exhibits temporally complex patterns of activity with features similar to those of chaotic systems. Theoretical studies over the last twenty years have described various computational advantages for such regimes in neuronal systems. Nevertheless, it still remains unclear whether chaos requires specific cellular properties or network architectures, or whether it is a generic property of neuronal circuits. We investigate the dynamics of networks of excitatory-inhibitory (EI) spiking neurons with random sparse connectivity operating in the regime of balance of excitation and inhibition. Combining Dynamical Mean-Field Theory with numerical simulations, we show that chaotic, asynchronous firing rate fluctuations emerge generically for sufficiently strong synapses. Two different mechanisms can lead to these chaotic fluctuations. One mechanism relies on slow I-I inhibition which gives rise to slow subthreshold voltage and rate fluctuations. The decorrelation time of these fluctuations is proportional to the time constant of the inhibition. The second mechanism relies on the recurrent E-I-E feedback loop. It requires slow excitation but the inhibition can be fast. In the corresponding dynamical regime all neurons exhibit rate fluctuations on the time scale of the excitation. Another feature of this regime is that the population-averaged firing rate is substantially smaller in the excitatory population than in the inhibitory population. This is not necessarily the case in the I-I mechanism. Finally, we discuss the neurophysiological and computational significance of our results.\n\nAuthor SummaryCortical circuits exhibit complex temporal patterns of spiking and are exquisitely sensitive to small perturbations in their ongoing activity. These features are all suggestive of an underlying chaotic dynamics. Theoretical works have indicated that a rich dynamical reservoir can endow neuronal circuits with remarkable computational capabilities. Nevertheless, the mechanisms underlying chaos in circuits of spiking neurons remain unknown. We combine analytical calculations and numerical simulations to investigate this fundamental issue. Our key result is that chaotic firing rate fluctuations on the time scales of the synaptic dynamics emerge generically from the network collective dynamics. Our results pave the way in the study of the physiological mechanisms and computational significance of chaotic states in neuronal networks.

Neuroscience↗

bioRxiv2015

Olfactory receptors are sensitive to molecular volume of odorants

To study olfaction, first we should know which physical or chemical properties of odorant molecules determine the response of olfactory receptor neurons, and then we should study the effect of those properties on the combinatorial encoding in olfactory system.\n\nIn this work we show that the response of an olfactory receptor neuron in Drosophila depends on molecular volume of an odorant; The molecular volume determines the upper limits of the neural response, while the actual neural response may depend on other properties of the molecules. Each olfactory receptor prefers a particular volume, with some degree of flexibility. These two parameters predict the volume and flexibility of the binding-pocket of the olfactory receptors, which are the targets of structural biology studies.\n\nAt the end we argue that the molecular volume can affects the quality of perceived smell of an odorant via the combinatorial encoding, molecular volume may mask other underlying relations between properties of molecules and neural responses and we suggest a way to improve the selection of odorants in further experimental studies.

Neuroscience↗

bioRxiv2015

Simultaneous Monitoring of Behavior & Peripheral Clocks in Drosophila Reveals Unstructured Sleep in an Alzheimer’s Model

Sleep and circadian rhythms are ancient, related phenomena controlled by distinct circuitry, whose appropriate regulation is critical to health. The regulatory machinery underlying sleep homeostasis is ill-defined, but biological clock mechanisms are better understood: from clock gene oscillations to rest/activity cycles. Age- and neurodegeneration-related deterioration in sleep/wake timing was first described in humans decades ago, and has recently been recapitulated in model organisms. To delineate causal relationships between aging, sleep, neuronal function and the molecular clockwork, we developed FLYGLOW, a bioluminescence-based system which allows rest/activity, sleep consolidation and molecular rhythms to be quantified simultaneously in many individual flies over days/weeks. FLYGLOW outperforms existing methods, and allows multiparameter correlational analyses within and between flies. We also show unambiguously that peripheral tissue rhythms free-run independently of the central pacemakers driving behavioural cycles. Using an Alzheimers fly model we observe a profound disorganization of sleep/wake cycles, phenocopying the human disease.

Neuroscience↗

bioRxiv2015

Oscillatory signatures of crossmodal congruence effects: An EEG investigation employing a visuotactile pattern matching paradigm

Coherent percepts emerge from the accurate combination of inputs from the different sensory systems. There is ongoing debate about the neurophysiological implementation of crossmodal interactions in the brain, and it has been proposed that transient synchronization of neurons might be of central importance. Specifically, oscillatory activity in lower frequency ranges (< 30 Hz) has been implicated in mediating long-range communication as typically studied in multisensory research. In the current study, we recorded high-density electroencephalograms (EEG) while human participants were engaged in a visual-tactile pattern matching paradigm. Employing the same physical stimulation, separate tasks of the experiment either required the detection of predefined targets in visual and tactile modalities or the explicit evaluation of crossmodal stimulus congruence. Analysis of the behavioral data showed benefits for congruent visual-tactile stimulus combinations. Differences in oscillatory dynamics within the two tasks related to crossmodal congruence involved effects in the theta-(2-7 Hz), alpha-(813 Hz) and beta-band (13-25 Hz). Contrasting neuronal activity between the two tasks revealed differences in pre-stimulus alpha- and beta-band power, as well as differences in post-stimulus theta-band activity. Source reconstruction for these effects showed prominent involvement of superior temporal, parietal and prefrontal cortices - regions commonly implicated in multisensory integration. These results add to the increasing evidence that low frequency oscillations are well suited for studying integration in distributed brain networks, as demonstrated for crossmodal interactions in visual-tactile pattern matching in the current study. Additionally, neuronal activity at theta-, alpha- and beta-frequencies might subserve distinct processes relevant for multisensory integration, such as multisensory gating and crossmodal perceptual decision making.

Neuroscience↗